Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature

Abstract

This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to the prediction of future breast cancer. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with the risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. A reduction in the number of features chosen for subsequent analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these features to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. Following these recommendations could enhance the applicability of models, helping improve risk classification and risk prediction for women to tailor screening and prevention strategies to the level of risk.

Modeling correlated pairs of mammogram images

“…The association between breast cancer risk and the patterns of breast parenchymal complexity has been widely recognized 24 . While mammographic density has been well‐studied to be positively associated with breast cancer risk, many more texture features are now being used that are complementary to the density measure 25 . To better account for the heterogeneity of breast composition between patients, it becomes crucial to model the pair of individual‐specific mammogram images.…”

Causal Mediation Analysis Using High-Dimensional Image Mediator Bounded in Irregular Domain with an Application to Breast Cancer

“…A systematic review of studies (Anandarajah et al., 2022) over the past decade or more indicates that approaches to identification and classification of texture features within mammogram images can better stratify risk. Most often, the full image is reduced to a few features or a summary score of some reduced number of features detected.…”

“…Furthermore, evidence supports breast density classified from the screening mammogram as an intermediate marker for breast cancer (Boyd et al., 2007). Continuing research to improve on the density classification (which is currently used either as a four‐level variable or a continuous measure) shows that additional texture features identified from the mammograms are also related to the risk of breast cancer (Nguyen et al., 2021; Anandarajah et al., 2022). For instance, our previously developed methods making use of the whole mammogram image showed improvement in long‐term risk prediction (Jiang et al., 021a, 021b).…”

Detection of Breast Cancer through the Analysis of Radiographic Images Using Machine Learning: A Systematic Review

“…Finally, the number of articles was reduced to 26, which will be used to develop the systematic literature review. From the different databases, eight articles published during the year 2023 were identified [1], [3], [4], [6], [11], [13], [14], along with eight articles from the year 2022 [2], [5], [7][8][9][10], [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], all of which met the filters established in the methodology, and that is why they were selected. The will above aids in conducting the review with data from up-to-date research grounded in knowledge from subsequent years.…”